GRAVITI: Grounded Retrieval Generation Framework for VideoLLM Hallucination Mitigation
Bibliographic record
Abstract
Video-language models (VideoLLMs) excel at tasks such as video captioning and question answering but often produce hallucinations-content not grounded in the video or metadata-limiting their reliability.To address this, GRAVITI (Grounded Retrieval GenerAtion framework for VideoLLM hallucInation miTIgation) is proposed; a model-agnostic, training-free and API-free framework that integrates a dynamically constructed ad-hoc knowledge base with a retrieval-guided decoding process.This process is referred to as Grounded Retrieval Generation (GRG), where each generated token is conditioned on evidence retrieved from video features and auxiliary metadata.GRAVITI reduces hallucinations while remaining compatible across diverse VideoLLMs.Evaluated on three benchmarks-VidHalluc, EventHallusion, and VideoHallucer-GRAVITI improves overall accuracy by 6-14% and substantially lowers hallucination rates compared to strong baselines.Ablation studies show the impact of retrieval size, detector thresholds, and grounding mechanisms, highlighting the effectiveness of GRG in producing reliable, multi-modal video descriptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".